A Polynomial-Time Descent Method for Separable Convex Optimization Problems with Linear Constraints
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Publication:2802141
DOI10.1137/14098524XzbMath1338.90299MaRDI QIDQ2802141
Publication date: 25 April 2016
Published in: SIAM Journal on Optimization (Search for Journal in Brave)
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Cites Work
- A strongly polynomial algorithm for linear systems having a binary solution
- A polynomial projection algorithm for linear feasibility problems
- Negative-cycle detection algorithms
- Optimal objective function approximation for separable convex quadratic programming
- An \(\varepsilon\)-relaxation method for separable convex cost generalized network flow problems
- Complexity and algorithms for nonlinear optimization problems
- Recent Advances in Linear Programming
- A Linear Programming Approach to the Chemical Equilibrium Problem
- Solving the Convex Cost Integer Dual Network Flow Problem
- An Extension of Karmarkar Type Algorithm to a Class of Convex Separable Programming Problems with Global Linear Rate of Convergence
- Inverse Optimization
- Computational aspects of two-segment separable programming
- Solving integer minimum cost flows with separable convex cost objective polynomially
- Updating the Inverse of a Matrix
- Polynomial Methods for Separable Convex Optimization in Unimodular Linear Spaces with Applications
- A Linearly Convergent Variant of the Conditional Gradient Algorithm under Strong Convexity, with Applications to Online and Stochastic Optimization
- Convex separable optimization is not much harder than linear optimization
